{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "plt.rc('font',**{'family':'Microsoft Yahei,Simhei'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、服务器器日志数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 请将数据导入pandas中，加上列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1  162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2  162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3  162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4  162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2017-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2017-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2017-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2017-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_table('log.txt',sep='\\t',names=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 检测是否有重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)-len(data.drop_duplicates())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 检测是否有异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a.响应时间总和是否有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_upper=data['res_time_sum'].quantile(0.75)\n",
    "q_lower=data['res_time_sum'].quantile(0.25)\n",
    "val=q_upper-q_lower\n",
    "k=1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6615"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[(data['res_time_sum']>q_upper+k*val) | (data['res_time_sum']<q_lower-k*val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b.响应时间平均值是否有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_upper=data['res_time_avg'].quantile(0.75)\n",
    "q_lower=data['res_time_avg'].quantile(0.25)\n",
    "val=q_upper-q_lower\n",
    "k=1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11689"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[(data['res_time_avg']>q_upper+k*val) | (data['res_time_avg']<q_lower-k*val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### c.单位时间内被访问的次数是否有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_upper=data['count'].quantile(0.75)\n",
    "q_lower=data['count'].quantile(0.25)\n",
    "val=q_upper-q_lower\n",
    "k=1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1205"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[(data['count']>q_upper+k*val) | (data['count']<q_lower-k*val)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分析api和interval这两列的数据是否对分析有用，如果⽆⽤，说明为什么后将这两列丢弃"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 答：这两个列数据完全一样，不值得分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
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       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  162542      8       1057.31         88.75        177.72         132.0   \n",
       "1  162644      5        749.12        103.79        240.38         149.0   \n",
       "2  162742      5        845.84        136.31        225.73         169.0   \n",
       "3  162808      9       1305.52         90.12        196.61         145.0   \n",
       "4  162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2017-11-01 00:00:07  \n",
       "1  2017-11-01 00:01:07  \n",
       "2  2017-11-01 00:02:07  \n",
       "3  2017-11-01 00:03:07  \n",
       "4  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=data.drop(['api','interval'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 使⽤created_at这一列列的数据作为时间索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-11-01 00:00:07</th>\n",
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       "      <th>2017-11-01 00:01:07</th>\n",
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       "      <th>2017-11-01 00:02:07</th>\n",
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       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
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       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2017-11-01 00:00:07         132.0  2017-11-01 00:00:07  \n",
       "2017-11-01 00:01:07         149.0  2017-11-01 00:01:07  \n",
       "2017-11-01 00:02:07         169.0  2017-11-01 00:02:07  \n",
       "2017-11-01 00:03:07         145.0  2017-11-01 00:03:07  \n",
       "2017-11-01 00:04:07         189.0  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.to_datetime(df.created_at)\n",
    "df.index=s\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-11-01 00:00:07</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_at                         \n",
       "2017-11-01 00:00:07         132.0  \n",
       "2017-11-01 00:01:07         149.0  \n",
       "2017-11-01 00:02:07         169.0  \n",
       "2017-11-01 00:03:07         145.0  \n",
       "2017-11-01 00:04:07         189.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop('created_at',axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-30 23:06:21</th>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 23:07:21</th>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 23:08:21</th>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 23:09:21</th>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-30 23:10:21</th>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                         \n",
       "2018-05-30 23:06:21  13438800     11       2783.48         99.24   \n",
       "2018-05-30 23:07:21  13438866     10       1951.10         85.37   \n",
       "2018-05-30 23:08:21  13438917      3        494.17        103.95   \n",
       "2018-05-30 23:09:21  13438981      9       1798.28        101.11   \n",
       "2018-05-30 23:10:21  13439086      6       1017.97         74.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-30 23:06:21        489.90         253.0  \n",
       "2018-05-30 23:07:21        529.51         195.0  \n",
       "2018-05-30 23:08:21        211.47         164.0  \n",
       "2018-05-30 23:09:21        433.30         199.0  \n",
       "2018-05-30 23:10:21        298.97         169.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 分析api调用次数情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df['2017-11-01']['count'].plot()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 分析一天中api响应时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "#df['2017-11-02']['res_time_min'].plot()\n",
    "#df['2017-11-02']['res_time_max'].plot()\n",
    "#df['2017-11-02']['res_time_avg'].plot()\n",
    "df['2017-11-02'].drop(['id','count','res_time_sum'],axis=1).plot()\n",
    "plt.xlabel('datetime')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 分析连续的几天数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "df['2018-05-01':'2018-05-11']['count'].plot()\n",
    "plt.xlabel('datetime')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 分析周末访问量是否有增加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df['2018-05-18']['count']#这天周五\n",
    "df2=df['2018-05-19']['count']#这天周六"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfa=df1.groupby(df1.index.hour).mean()#非周末数据采集每小时平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfb=df2.groupby(df2.index.hour).mean()#周末数据采集每小时平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IYRifnfvn4YZhbDAMY51hGL8zdPeJiIhIz+x7CxxN3V6+e3tXPj//+2EWT0nkX27s/v6py/RwGS8tPoLswhr31eNFOg1QhmEMAlYAYece+jrwhGmaC4Ak4KreK09ERMTPmabV+ylhKgzt+o/UTUfLePqv+7hmdDQ/u6sLvZ5cETEURsyGA29bdXZRekIkx0praWp1uK8mL+HKDJQDWAJUA5im+axpmtnnnosGynqpNhEREf+Xvx1Ks7s1+5RbVMPjf9rJ6NgwXn5oGkH2XtiZM/4OKMuFkuzOx14iLT6SVqfJkeJa99flYZ3+lzZNs9o0zbOXPm4YxhLgoGmaBe0895hhGDsMw9jRn69rERER6dTOFRAYBhPu6tLLis428sjybYQGBbD80ZlEhXax15Or0m8Hw9atzeQXXunib7oVVQ3DGA18B/h2e8+bpvk70zSnm6Y5PTY2tif1iYiI+K/Gaqvb91V3QbDrd8bVNrXy6B+3U93QwvJHZ5A4MLT3agyPg5FzrADVxWW8kdFhhATa/PIkXpcD1Lk9UW8CX2lvZkpERERcdOCv0FIPUx9x+SUtDidPvL6Tw8U1vPTgNMYn9KDXk6vGL4byI1B8sEsvC7AZjBsaqQB1zveA4cCvzp3Gm+fmmkRExN0+/zW8dA1s/z20+mdnaJ+0cwUMmQCJU10abpomz76zn8+OlPGTxROYN7aPVnnSbuvRMt6hgmrMbmxC92YuByjTNDPOfX3GNM140zQzzv3vk16rTkREem7Ly/DR96G+HD74Z/jlZNj6W2hp8HRl/VvhXijcY9175+LJuV+tO8r/7cjnWwtSWDLDTb2eXBEWA6PmdmsZLz0+gurGVgrONvZScZ6hRpoiIv5sxx/gw2dg3K3w1EF46F0YPApWPw2/nASbfwXNdZ6usn/auQLsITDxHpeG/3VnPi+uOcydUxN56gY39npy1fjF1lUzRfu69LK2juR+1lBTAUpExF/t+TO8/xSMuQnuXg4BgZA8Hx7Ngkc+gNhx8PcfwLKr4LMXock/Gx56peY62P8XSL8DQgd1OnzjkTK+97d9zE6J5qd3urnXk6vGLQIjoMvLeOP89EoXBSgREX+0/6+w8kkYnQH3vgb2oIufHzkHlq6Cr66xGjh+/Dz89wTY8DNoqPJExf3LwXehqdql3k/ZhdU8/vpOkmPD+c2DvdTryRVh0dafpy4u44UH2xkRPcDvWhkoQImI+Jvs9+Dtx6yLYO97EwJDrjw2aSY8+Ff4h3Uw4lrY8BNrRmrdC9b9bNI7dq2A6DHW71EHSmoaeXT5dsKCA1j+6AwiQ3qp15Orxi+GypPW3q0uSPPDk3gKUCIi/uTwR/CXRyFxGtz/lusX0yZOgy+/CV//zFrm+/S/rCC15l+hVg2R3aokG/K2wtSHO9w87nCafOvN3VQ1NPOHR2aQ0Ju9nlw17haw2bu8jJcWH8mpinrqmlp7qbC+pwAlIuIvjq2Dtx6CoROsWaUuNGZsEz/RWvL7xhYY+yVrk/myq+DD70NNkftr7o92vQa2QJh8f4fDlq09zJbjFbxwx1V90+vJFQMGw+j5XV7GS0+IxDQhp8h/9tkpQImI+IOTG+HN+yFmDDz4NoT08AduXBrc/Xt4cru1bLP1ZVg2ET74DpzNd0/N/VFLI+x905rJCYu54rBPDpfyP+uPcs+0Ydw9bVgfFuiC8Yuh6jQU7HL5JWnxVpj3p31QClAiIr7u9FZ4414YONxqUzBgsPveOyYFFv8G/nEnTFoCO5dbfaRWfcvaCyNdk/M+NFR2uHm88GwDT721h7FxEfz49gl9WJyLxt1szaAdeNvllyQODCUyxO5X+6AUoEREfNmZXfDG3RAxxDpVF95LnakHj4LbfgXf2m3t3dn7Jvx/U+Hdb0D5sd75TH+0848wcASMymj36RaHk3/8826aWhy89OBUQoMC+rQ8l4QOguQF1klCF5fxDMNgXLx/bSRXgBIR8VVF++FPiyF0ICx9DyKG9v5nDhwOt74I/7QXZj4GB/4GL18H1YW9/9m+rvwYnPwMpj4EtvZ//P7877nsOFXJT+68iuTY8D4usAsm3AnV+ZC/w+WXpMdHkltUg8PpH1e6KECJiPiikhx47XYICrPCU1Qf75OJTICFP4XHNkBLHez9c99+vi/a9ZrViHLyg+0+vfZQMb/95DgPzBrO7ZMT+7i4LkpdCAFBXTqNlx4fSX2zg1Pl/tH5XgFKRMTXlB+D126zjpMvfQ8GjfRcLXFpMGIO7PoTOJ2eq8PbOVqszvBjb4LI+Muezq+s51/+spfxCZE8d2u6BwrsopAoSMmEQ++6/PvedqVLoX+cxFOAEhHxJZUnYcUicDrg4VUQnezpiqw9UZUn4NRGT1fivQ5/CHUl1sXBl2hudfLkn3fjdJr8+v6phAR64b6n9oxfDNVnIH+7S8PHDAknwGb4zT4oBSgREV9xNt8KTy318PBKiBvn6Yos6bdZMxI7V3i6Eu+1cwVEJFizNpf4j9XZ7M2r4j/vnsjImDAPFNdNY78EAcEuL+OFBAaQHBumACUiIn2opsgKTw1V8NA7VrNMbxEYChOXQPYqXf/Snqo8OLoWpjwIAfaLnvrwQCHLN53kkWtHsvCqy5f2vFpIJIy5ocvLeP7SC0oBSkTE29WWworboKYYHvwbJEzxdEWXm/owOJph3/95uhLvs/t16+vUhy56+FR5Hd/9yz4mJQ3k+zeneaAwNxi/GGoKIW+LS8PT4iMpPNtIVX1zLxfW+xSgRES8WX2Fddqu6jQ88H/W5b/eaOhVVrDbtaJLV3z4PafDClDJC6wWEOc0tjh48s+7MAz4ny9PIcjuoz+Ox34J7CEuL+Od30juD7NQPvo7JiLSDzRUWX2eyo9aF/2OnOPpijo2dSmUHLKae4rl6MdWv6RLOo+/8MEhDpyp5hf3TiZpsIsXPnuj4HAYcyMcWmmFxU6k+9FJPAUoERFv1FRjdRgvPghL/gTJ8z1dUecm3AWBA2DXHz1diffYtQIGxMDYhW0Prdxzhte3nOaxuaO5IX2IB4tzk/GLobYYTn/e6dDYiGBiwoM5VKAZKBERcbfmevjzEmsm557lVu8gXxASCePvhP1/swJgf1dTBLmrYfL9YA8C4FhpLd9/ez/TRgziuzelerhANxl7E9hDu7CMF+EXJ/EUoEREvElLI/zvl62/zd/1CqQt8nRFXTP1YaszeRc6VPutPW+A6Wjr/dTQ7ODJN3YRZLfxP/dPITDAT34EB4VZIaoLy3hHS2ppcfh241U/+d0TEfETn/4nHN8At//aWhLzNUkzISbVurakP3M6rf8GI+ZATAoAP1p1kJyiGv57yWTio0I9XKCbjV8MdaVwalOnQ9MTIml2ODlWWtsHhfUeBSgREW9yaCWMnm8t+/giw7A2TOdvh+JDnq7Gc05+anWNP7d5/K8783lrRx5Pzk8mIzXOs7X1hjE3QmAYHHi706FtJ/F8fB+UApSIiLcoPWyduBt3i6cr6ZmJ94EtsH/PQu1cASEDIe02DhfX8IN39zNr1GCeyhzr6cp6R9AASP2S1UzV0drh0NExYQTZbT6/D0oBSkTEW+RmWV9TF3Y8ztuFRUParbDvf609Xf1NXTnkvA+T7qPOaecbb+wiPNjOr748Bbu/7Htqz/jFUF8OJz/rcJg9wMbYIeE+38rAj38nRUR8TG4WDJ0IUcM8XUnPTX0YGiqtINHf7PtfcDRjTn2YH7x7gGOltfzyvinERYZ4urLelZIJQeEuHSBIj48ku7Aa04ebripAiYh4g9pSyNvm+8t3543KsDpv97dlPNO0lu+GzeCtUxG8s/sM375+LLNTYjxdWe8LDLVmT7PfA0dLh0PT4iMpr2umpKapj4pzPwUoERFvcPhDwPT95bvzbDaY8jCc+AQqTni6mr6TtxXKcikYfS//uuogc1Ji+OaCFE9X1XfGL4aGCuv3vQP+cKWLApSIiDfIXQ2Rw6wlPH8x+X4wbLD7T56upO/sXIEZFM5Xdw5nYGggy+6bTIDN8HRVfSf5eqsb/eG/dzgsre1KFwUoERHpruZ6OLbOmn0y/OiHbVQipNwAu9/o9GSWX2iowjz4DhtD5pNb4eBXX55CTHiwp6vqW4EhMPI6OLq2w2FRoYEkDgz16Y3kClAiIp524hNobYBxN3u6Eveb+jDUFsHRNZ6upPft/wtGawM/K53Fd25KZdboaE9X5BkpmVBxDCqOdzgsLT6SQwVn+6go91OAEhHxtJwPIDjS6lrtb8beBGFx/r+Z3DRp2PoHDpojiR07i8fnJnu6Is9Jud76evTjDoelx0dwoqyOxpbOr3/xRgpQIiKe5HRaG8hTMtsunPUrAYEw5QE4/BFUF3q6ml5Tn/sxoeWHWB14Ay/eOxlbf9r3dKnoZBg0qtNlvPSESJwm5Bb55jKeApSIiCed2WHdIZbqh8t35015yLpUd88bnq6kd9QUYbz9GMec8cy751sMCvPDINxVKZlw4lNovXKbAl/fSK4AJSLiSblZYLPDmExPV9J7opOtjcW7/2TNuPkTRyv89avYmmv5WeT3mT42ydMVeYeUTGiph9OfX3FI0qABhAUF+GwrAwUoERFPysmCEddC6CAAquqb2Xy0jOZWPwsaUx+2Ltft5JoPn7Pu3+DURr7X/BUmTr0Gw59OUfbEyDkQENThMp7NZjDuXEdyX6QAJSLiKeXHoCwXUr/oPv4fWTnc/+pWZv1kLf+68gC7T1f69HUXbdIWQUiUf20mz8mCTcs4lHAn7ziv47ZJiZ6uyHsEh8Pwa1zYSB5JTmGNT/4ZV4ASEfGU3NXW13Pdx1scTj48WMSsUYOZnRLDW9vzWPzSZhb84hN+ufYIp8vrPVhsDwWGwsT7IHsV1Fd4upqeqzgB7z4O8ZP4Xv2DTE4ayPDoAZ6uyrukZELJITh75opD0uIjqWlqJb+yoQ8Lcw8FKBERT8nNgiETYNAIADYfK+dsQwtfu240/3P/VLb/IJP/vHsiQyNDWPbxYeb+13ru/s1mXt9yiqr6Zg8X3w1THwZHM+x7y9OV9ExLI/xlKQAnFvyGfUWN3DYpwcNFeaGUc/v6jl15FiotPgKAgwW+t4ynACUi4gn1FdYG2wvuvsvaV0h4sJ3rxlgXz0aGBHLv9CTefOxqNj2zgKe/lMrZhhZ+8O4BZv77x3z9Tzv48EARTa0+0kdn6ARImGot4/ngkk2bD5+Bwr1wx8u8fcKOzYBbJ8Z7uirvE5cGEQkd7oNKHRqBYfjmSTy7pwsQEemXDn8EprOtfUGLw8lHh4q4Pi2OkMCAy4YnDAzlGxkpPDEvmYMF1byz+wwr9xTw0cFiokIDuXViPIunJDJtxCDv3sg89WF4/9twZicMm+7parpuz5uw848w+9uYqQtZ9d4GrkmOJi4yxNOVeR/DsE6XHlxpnVYMuDxyDAiyMyomzCcDlEszUIZhDDEM47Nz/xxoGMZ7hmFsMgzjK71bnoiIn8rNgoh4iJ8MwJbj5VTVt3DzVR3PZBiGwYTEKJ67NZ0t/28Bf3x0BhmpsfxtVz53v/w58/5rAy+uOcyJsrq++FV03YS7rMtmd63wdCVdV3wQ3n/K6hi/4Dn25Z/lVHk9t2vz+JWlZELTWcjffsUhafGRZBf5YYAyDGMQsAIIO/fQPwI7TdOcDdxtGEZEL9YnIuJ/Whqt00mpC8FmfRvO2l9EWFAA88bGuvw29gAbGalx/PK+Kez4wQ384p5JDB88gF+tO8L8n2/gjl9v4rXPT1JR50X7pUIiYcKdsP9v0ORDHaibauD/Hrbqv/sPEGBn5Z4CggJs3DRhqKer816j5oER0OEyXnp8JHkVDVQ3tvRhYT3nyhKeA1gCrDz37xnA987986fAdGD9hS8wDOMx4DGA4cOHu6NOERH/cfIzaKlrW75rdTj56GARC9KGtLt854rwYDt3TRvGXdOGUXS2kVV7z/D2rjP868qD/Pi9Q2SkxnF9WhzBdvdvfY2NCOa6Ma4HP6Yuhd2vw4G3YdpSt9fjdqYJK79pXY679D2IGILDafL+vgIyUmOJCg30dIXeK3QgJM20AtT1z7U75PxG8pzCGmaOGtyX1fVIpwHKNM1q4MI19TBkYp9qAAAgAElEQVTg/JnECmBIO6/5HfA7gOnTp/vwTkERkV6Q8wEEhcOouQBsPVFBRV0zN7tpJmNoVAiPzU3msbnJZBee3y91hrXZxW55//b87YlrmDbCxR9+w2ZA7DhrM7kvBKitv4VD70Lmj6wGkcDW4+WU1DRx22SdvutUyvWw7gWoLYXwy4N2enwUYG0k96sA1Y5aIBQ4C4Sf+3cREXHF+cuDkxeAPRiArP2FhAYGkJEa5/aPS4uPJC0+kme+NI4zlQ2YuPfvtC0OJ/f+dgvL1h7hT1+d5dqLDMPaTP7R9619RUPGu7Umt8rbBn9/1potvPaf2h5etbeAsKAArh932RyCXCol0wpQx9bBpCWXPT0kMphBAwI55GOtDLoToHYCc4C/ApOALW6tSETEnxXuhppCGGd1H3c4zXPLd3GEBnVv+c4VATaj1xo9PjZ3ND9dncPOUxWuz0JNvA/W/gh2/QkW/rRX6uqxunL4yyMQmQh3vNS2X62p1UHW/kJuHD+0V3/P/MbQSTAgxlrGaydAGYbhkxvJu7MYvgJ43jCMXwLpwFb3liQi4sdyV1ubasfcCMC2ExWU1TZz8wTf7SP08DUjGBwWxLK1R1x/UVg0jLsV9v2vtane2zgd8PbXoK4M7n2t7a5CgE8Pl1Hd2Krmma6y2axlvGMfX/Ey6bT4SHKLamh1+M4dkC4HKNM0M859PQXcAGwCMk3T9JEObiIiXiAny7ojbIA1U5O1v5CQQBvzx3VhE7aXGRBk5+tzR/PZkTJ2nOzCNS1TH4aGSsh5v/eK665P/8taclr4M0iYfNFTq/YWMGhAIHPONTwVF6RkQn05FO5p9+n0+EiaWp2cLPfS9hvt6NZxDNM0C0zT/D/TNM+6uyAREb9VeRJKDrZ1H3c4TVYfKGJ+ahwDgny7r/FD14wguquzUKPmwcDh3tcT6ujHsOGnMOnLMO2Ri56qa2plzaEibr4qnsAAXebhsuQFgHHFy4XT4iMB37rSRb/7IiJ95fzlweOs9gU7TlZQVtvUafNMXzAgyM7X541m49Eytrs6C2WzwZSH4cSnVosAb3A2H/72Nesaklt+YW14v8Da7GIaW5xavuuqsBhrJu8K/aBS4sIJDDDILvSd3mAKUCIifSU3yzq+P3g0YC3fBdttLBjn/tN3nvDg1SOICQ9i2drDrr9oygNg2Ky+UJ7W2mxtGnc0W/uegsIuG7JyTwHxUSHMGOk7x+29Rkom5G+zlm0vEWS3kRwb7lNXuihAiYj0hYZKOLmprXmm89zyXUZqLGHBvr18d561FyqZTUfL2XbCxVmoyARrQ/3uN6z70jxpzb9aV47c/j8QM+aypyvrmvn0cCmLJiVgs3nxfYPeKuUG6/7H4xvafTo9IVIBSkRELnFkLZiOtgC183QlJTX+sXx3oW7NQk19GGqL4Mjfe6+wzhx8B7b+BmY9AeMXtzsk60AhrU5Ty3fdlTgNQqKuuIyXHh9JSU0TZbVNfVxY9yhAiYj0hdwPICzO+iECfLCvkCC7jevT/KsRY2hQAI/PS2bzsXK2Hi937UVjboTwIVZnck8oO2Jd1TJsBtzw4ysOW7WngNGxYYxPiOzD4vxIgB1Gz7c2kpuXN3Q9v5HcV2ahFKBERHpba7M1A5X6JbDZcDpNPjxQxLyxsYT7yfLdhR6YNYKY8GDXT+QFBMLk++HIR1Bd0LvFXaq5zrokOCAI7vkj2IPaHVZ4toFtJyu4fVLihVebSVelZFqNZEsOXfaUApSIiFzs5GfQXAOpVvfx3XmVFFU3coufLd+dZ81Cjebz4+VscXUWaspD1v6YPX/u3eIuZJrw/j9DSTbc9SpEDbvi0Pf3FmKa6O67nkq53vrazjLe4LAghkaG+MxJPAUoEZHelrsaAgfA6HkAZO0vIijAxoI0/zh9154Hrx5BbEQwv3R1Fio6GUZeZy3jXaFbtdvtWmF1Qs/43hc/2K9g1d4CrkqMYlTM5SfzpAsiEyBu/BX3QaXFR/jMnXgKUCIivck0rQCVvAACQ63Td/sLmTs2hsiQQE9X12tCAq29UF2ahZq6FKpOwclPe7c4gII9kPW09fsy97sdDj1eWsv+M2e5XbNP7pFyPZz6HJpqL3sqLT6SY6W1NLV6/yUnClAiIr2paB9U57d1H9+TX0XB2UYW+vDdd656YNZwYiOCXT+Rl7YIQgb2/mbyhkpr31NYDNz5Ctg6vhB41d4CDANunagA5RYpmeBssZa2L5EWH0mr0+RI8eXhytsoQImI9KacLMCAsV8CYPX+QgIDDDLT/ev0XXtCAgN4Yl4yW45X8PkxF2ahAkNg4hLIfg/qu3CnXle0NMC734DqM9am8bCO77MzTZNVewuYNWowQ6NCeqem/mb41RAY1u4yXnqC72wkV4ASEelNuVmQNAvCYjBNk6z9RVw3JpaoUP9dvrvQ/bOGE9eVWaipD1udwPe91fMPb2mAvO2w7RV490n4zWz4SaL1e3Ljv0PSzE7f4mBBNcdL67htUmLP6xGLPRhGzYUjay5rZzAyOoyQQBuHfCBA+d/5WRERb1GVZy3hnesttC//LGeqGvh25uVdrv1VSGAAT2Qk8/x7h9h8rIxrkzue8WHoBKtX1s4VMOvxy+6iu6Lmeig+YO1tKtxjfS3NsZqXAgyIhvjJMPYmGH6NtYzkglV7CwgMMFg4YahrdYhrUq6Hw6uh/BjEpLQ9HGAzSB3qGx3JFaBERHrL4Q+tr+e6j2ftL8RuM7gxvX/9MP7yzOH8ZsMxlq09wjWjozvvozT1YXjvnyB/ByTNuPz55noo2v9FUCrcA6W5F4SlcxfXpi60vsZPtloUdLF/k9Np8t7eAuaOiWVQWPv9oaSbzgfYo2svClAA6fERZO0vwjRNr+65pQAlItJbcj6A6DEQMwbTNPlgfyGzU2KIGtA/lu/OCwkM4BsZyfzovUN8fry881moCXfBh9+32gwMGX9xWCrYDWW5Vs8ogLBYKyCNu8X6mjAZIhO7HJbas/1kBYVnG/newnE9fi+5xOBREJ1iBairH7/oqfT4SN7clkfh2UYSBoZ6qMDOKUCJiPSGxrNwciNc/QQAB85Uk1/ZwLcW9J/luwvdN3M4v/nkGMvWuDALFRwBExZbTTX3vHFBWIqzAlLaoi9mliIT3BKW2rNybwGhgQFk+tl1O14jJdNaqm1pgMAvgtL5juSHCqoVoERE+p2ja62j2uOs7uMfnF++G98/fxhbs1Ap/HDVQT4/Vs61KZ3MQs1+ylqqixnzxcxSRHyvhaVLtTicrN5fSGb6EML88Lodr5CSCVtfhlObL2pkOu6CK128+bSqTuGJiPSG3NXWXpxhMzBNk9UHCrkmOZqBA/rvXpolM5IYGhnCf689jNnOZbIXiUmBe5bD/O/DuJt7daapPRuPlFFZ38Ltk9T7qdeMmA0BwdblwhcID7YzfPAAsou8eyO5ApSIiLs5WuDI363eT7YADhZUc6q83m/vvnNVSGAA35ifzPaTlWx2pS+UB63cc4ao0EDmjo31dCn+K2gAjJzdfj+o+EivvxNPAUpExN1Obbb2QJ3rPp61v5AAm8GN4/vX6bv23DvdmoVa5soslIc0NDv4+6FiFk4YSpBdPyZ7VUqmdSig6vRFD6fFR3KyvI66plYPFdY5/ckQEXG33Cywh0Dy/HPNMwu5ZnQ0g3UUnpDAAJ48Nwu16ah3zkKtzS6mvtnBbbr7rve1tTO4eBkvLT4C04ScIu+dhVKAEhFxJ9O0AtToDAgKI7uwhpPl9dzcz5fvLnTvjCTio7x3FmrV3gLiIoKZNSra06X4v5ixEJV02TJeWrz3X+miACUi4k7FB63liHPNM1cfKMRm0G9P37Un2B7AN+ansONUJRuPlnm6nIucrW9hQ24JiyYlEGDz3iaOfsMwrBN4xz+B1ua2h4cNCiUixK4AJSLSb+Su5vzlweebZ149OpqY8GBPV+ZV7p0+jISoEJatPeJVs1AfHiykxWFym07f9Z2UTGiugfxtbQ8ZhkFafKRX34mnACUi4k65H8Cw6RAxhNziGo6X1rFQy3eXOT8LtfNUJZ8d8Z5ZqFV7CxgZPYCJw6I8XUr/MWoe2OyXLeOlx0eSW1SD0+k9AftCClAiIu5SXWBdNdJ2+q4ImwFf0um7dt3TNgvlHXuhSqob2XysnNsmJXj1HWx+JyQSkq5uN0DVNzs4VVHvocI6pgAlIuIuuautr6lW9/Gs/YXMHDWY2Agt37Un2B7AkwtS2HW6yitmod7fV4hpotN3npByvXXnYU1R20PevpFcAUpExF1yV8OgURCbypHiGo6W1Or0XSfumZZEQpSL3cl72cq9BaTHR5ISF+HROvql8+0Mjq1re2jMkHACbAaHChSgRET8V1MNnPjEuvvOMPhgfyGGlu86FWS38eSCFHafruJTD85CnSqvY29elWafPGXoVRA+5KJlvJDAAEbHhGkGSkTErx1bB47mi7qPzxgxmLjIEA8X5v3umZZE4sBQ/nuN52ahVu0pAGCRTt95hmFA8vXW/4+cjraH0xMiFaBERPxaThaEDoKkqzlaUsPh4lpuvkqzT64Istt4cn4Ke/Kq+ORwaZ9/vmmarNpbwIyRg0gcGNrnny/npFwPDZXWQYxz0uIjKTjbSFV9cwcv9AwFKBGRnnK0wpGPYMxNEGAna7+1EVbtC1x397Rh1iyUB/pC5RTVcKSkltsmJ/bp58olkhcAxkXLeOc3kntjPygFKBGRnsrbYv3NeZzVfTxrfyHTRwxiiJbvXBZkt/HNBSnszatiQx/PQq3cU0CAzeDmCZox9KgBgyFx2iUBytrQn13ofXfiKUCJiPRU7moICILkBRwvrSWnqEan77rhrqnDGDYotE+7kzudJu/tLWBOSgzR6hbveSmZkL8D6isAiIsIISY82Cv3QSlAiYj0hGlCzgdWN+XgCLL2FwKwUPufuizIbuOb88/NQuX2zSzUrtOVnKlq4HadvvMOKZmAeVE7g7T4CAUoERG/U5oLlScu6j4+dfhA4qO0Gbk77pp2fhaqb07krdpbQLDdxo1qN+EdEqdahzGOftz2UHp8JEeKa2lxOD1Y2OUUoEREeiL3A+tr6kJOltVxqLBay3c9EBhg4x8XpLA3/yzrc0t69bNaHU4+2FdIZtoQwoPtvfpZ4iJbgLWZ/OhacFqBKS0+kmaHk2OltR4u7mIKUCIiPZG7GhKmQGQCWQfOL98pQPXEnVOHkTQ4lBfXHKayrveOr286Vk55XbN6P3mblEyoK4HiA4DVCwq870qXLgcowzAGGYaRZRjGDsMwftsbRYmI+ISaYmvD6wV3301OGqheQj0UGGDjOzemcuBMNTN/spZ/eG0HWfsLaWxxdP7iLli1p4CIEDsZqbFufV/poeQF1tdzp/FGx4QRZLd53ZUu3ZmBegh4wzTN6UCEYRjT3VyTiIhvOPwhYELqQk6X13PgTLWaZ7rJ7ZMTyfrWdTxy7Uj25lXxjTd2MePf1/L/3t7HthMVOJ092x/V2OLgo4NFfGn8UEICA9xUtbhFxFDrapdz+6DsATbGDgn3ulYG3Vn0LQcmGIYxEEgC8txbkoiIj8jNgoHDYch4sj49DsDCCVq+c5f0hEjSE9L53sI0Nh8r451dZ1i5p4A3t+UxbFAod0xOZPHURJJjw7v83utzSqhtatXdd94qJRM2/woaqyEkkrShkazLKcE0TQzD8HR1QPdmoDYCI4BvAdlAxaUDDMN47NwS347S0r5vyy8i0uua6+D4Bki9GQyDrP2FTBoWRdLgAZ6uzO8E2AyuGxPLi0sms+MHmSxbMpnRseG8tOEo1//iE277n40s33SCstoml99z5Z4CYsKDuWZ0dC9WLt2WkgnOVjjxKWCF6fK6ZkprXP897m3dCVA/BB43TfPHQA7w6KUDTNP8nWma003TnB4bq7VlEfFDx9ZDayOk3kxeRT378s9q83gfGBBk544pibz2lZls+X/X84Nb0mh1mDz/3iFm/eRjvvLH7by3t6DD/VLVjS2syy3h1onx2AN0lsorDZsJQRFt+6DOX+ly0Is2kndnCW8QcJVhGFuAWcDaTsaLiPifI3+H4CgYcS2rN50G4GYt3/WpuMgQvnbdaL523Whyi2p4Z/cZVu45w7qcEsKD7SycMJTFUxO5elQ0NtsXyz5/P1hMc6tTy3fezB4Eo+dZAco0SRv6xUm8+alxHi7O0p0A9R/AcqxlvM+BN91akYiIL8jbCsNnQUAgH+wvYkJiJMOjtXznKalDI/jewnE8fVMqW46X887uM6w+UMRfduaTEBXC7VMSWTwlkbFDIli55wxJg0OZkjTQ02VLR1Kuh5z3oewwUbGp/O6haVw1LMrTVbXpcoAyTXMbML4XahER8Q31FVCaA1fdQ35lPXvzqnj6S6merkoAm83g2pQYrk2J4ce3T2BNdjHv7Mrnd58e5zcbjjE+IZKcohq+Pne012xGlitIvt76enQtxKZ6Xbd4tV4VEemq/B3W16RZfHigCNDynTcKDQrgtkkJ3DYpgdKaJt7fV8A7u88QYDO4c+owT5cnnRk0AmLGWgHqmic9Xc1lFKBERLoqbwsYAZA4jazVu0mPj2RkTJinq5IOxEYE8+jsUTw6exQtDieB2jzuG1JugO2vQnM9BHnXErn+BImIdFXeNoifSEG9wa7TVdwyUbNPvkThyYekXA+OJji1ydOVXEZ/ikREusLRYi3hJV3N6nPLdwsneNfeDBG/MWI22EPb2hl4EwUoEZGuKNoPrQ2QNJPV+wsZNzSC0d3ohC0iLggMgZFzFKBERHxe3lYASgZOZsepSm5R80yR3pWSCeVHoeKEpyu5iAKUiEhX5G2FqCQ+OGUdgVf3cZFelpJpfT32sWfruIQClIiIq0wTTm/FTJrFm9tOMz4hkpQ4Ld+J9KroZBg4Ao4qQImI+KazeVBTwLGQdA4X1/Lo7FGerkjE/xmGNQt1/BNo9e3LhEVE+qe8bQC8diaemPBgFk3S8p1In0jJhJY6OL3F05W0UYASEXHV6S047QN440Q4D18zgmB7gKcrEukfRl0HtkA4/bmnK2mjTuQiIq7K28rxkDQCGgN5YNZwT1cj0n8ER8C3dkFUkqcraaMZKBERVzTVYBYf4KPqEdw5JZHo8GBPVyTSvwwcbu2H8hIKUCIirjizE8N0srV1DF+Zo83jIv2dApSIiAtaT27BiUHIqKsZOyTC0+WIiIdpD5SIiAvKsz+lwjmM++dO8HQpIuIFNAMlItIJ09FKeNlujgaPZ97YWE+XIyJeQAFKRKQT+/ZsJcysJzptLoYXbWIVEc9RgBIR6cT+z/8OwLQ5N3m4EhHxFgpQIiIdOFlWx4DiHdQFDiY4NtnT5YiIl1CAEhHpwB83n2S67TABI672qh40IuJZClAiIldwtqGFdTv2M9woIWT0tZ4uR0S8iAKUiMgVvLX9NGmt2da/JM3ybDEi4lUUoERE2tHqcLJi8yluHZQHAcEQP8nTJYmIF1GAEhFpx4cHizhT1cB1IccgcSrYdfediHxBAUpEpB2/33iCsYPtRFUehKSZni5HRLyMApSIyCV2na5k9+kqnhpfh+FsgaSrPV2SiHgZBSgRkUv8fuMJIkPsXB9+0npAM1AicgkFKBGRC5ypauDDA0V8eeZwggq2Q3QKhMV4uiwR8TIKUCIiF1ix+SQAS68ZAXlb1b5ARNqlACUick5dUytvbjvNwglDSXAUQH25ApSItEsBSkTknL/syKOmsZWvzhllzT6BApSItEsBSkQEcDhNlm8+ydThA5kyfBDkbYGQgRAz1tOliYgXUoASEQE+zi7mVHk9X50z2nogb5t1+s6mb5Micjl9ZxARwWpdkDgwlJvGD4H6CijN0fKdiFyRApSI9HsHzpxl64kKHrl2JPYAG+TvsJ5QgBKRK1CAEpF+7w8bTxAWFMCSmUnWA3lbwAiAxGmeLUxEvJYClIj0ayXVjby3r4B7picRGRJoPZi3DeInQtAAzxYnIl5LAUpE+rXXPj9Fq9Pk0dkjrQccLdYSnu6/E5EOKECJSL/V2OLgja2nuCFtCCOiw6wHi/ZDa4PuvxORDnU7QBmG8ZJhGIvcWYyISF96e9cZKutbrMaZ56mBpoi4oFsByjCM64Chpmm+5+Z6RET6hGma/GHTCSYkRjJz1OAvnsjbClFJEJXoueJExOt1OUAZhhEIvAKcNAzjdveXJCLS+z45XMrRklq+OmcUhmFYD5omnNYFwiLSue7MQD0MHAL+E5hpGMY/XjrAMIzHDMPYYRjGjtLS0p7WKCLidr/feIK4iGBuuSrhiwfP5kNNgQKUiHSqOwFqCvA70zSLgNeB+ZcOME3zd6ZpTjdNc3psbGxPaxQRcavDxTV8dqSMpdeOJMh+wbfB8/ufhitAiUjHuhOgjgLnLotiOnDKfeWIiPS+P2w8QUigjftnDr/4ibytEBgGceM9U5iI+Ax7N17ze+APhmHcBwQCd7u3JBGR3lNe28Tbu89w97RhDAoLuvjJ01tg2HQI6M63RhHpT7r8XcI0zRrgnl6oRUSk172x9TTNrU6+MnvUxU801ULxAbjuO54pTER8ihppiki/0dTq4LXPT5GRGktKXPjFT57ZAaZT+59ExCUKUCLSb7y3t5Cy2qaLG2eel7cNMGDYjD6vS0R8jwKUiPQLpmny+40nSB0SwZyUmMsHnN4CcekQEtX3xYmIz1GAEpF+4fNj5WQXVvOVOSO/aJx5ntMJ+dt1/52IuEwBSkT6hd9vPEF0WBC3T27nipbSbGiqhuFX931hIuKTFKBExO8dL63l45wSHrh6BCGBAZcPaLtAWDNQIuIaBSgR8XvLN50kKMDGQ1ePaH/A6a0QFgeD2tlcLiLSDgUoEfFrVfXN/HVnPrdNTiA2Irj9QXlbrdmnS/dGiYhcgQKUiPi1/92eR0OLo/3WBQC1JVB5QvufRKRLFKBExK+9t7eAaSMGkRYf2f6Atv1PaqApIq5TgBIRv1Vc3cjBgmquT4u78qDTWyAgGOIn9V1hIuLzFKBExG+tzykBYMG4DgJU3lZImAL2K+yPEhFphwKUiPit9bklJESFkDokov0BLY1QsEf334lIlylAiYhfamp1sPFIGRnj4i7vPH5ewW5wtmj/k4h0mQKUiPil7ScqqWt2sCC1k+U7UIASkS5TgBIRv7Qup4Qgu41rU6KvPChvKwxOhrB2LhcWEemAApSI+KUNuSVcMzqaAUH29geYphWg1P9JRLpBAUpE/M7JsjqOl9UxPzX2yoPKj0F9ue6/E5FuUYASEb+zrq19wZArD2rb/6QZKBHpOgUoEfE763NLSI4NY3j0gCsPytsCIVEQM7bvChMRv6EAJSJ+pa6pla3HKzpungmQt806fWfTt0ER6Tp95xARv7LpaBnNDifzOwpQ9RVQmqP9TyLSbQpQIuJX1ueWEB5sZ/qIwVcelL/D+qr9TyLSTQpQIuI3TNNkfU4p142JIcjewbe3vC1gBEDi1L4rTkT8igKUiPiN7MIaiqobO16+A2v/U/xECArrm8JExO8oQImI31ifa7UvyOio/5OjxVrC0/UtItIDClAi4jfW5ZRwVWIUcREhVx5UtB9aGxSgRKRHFKBExC9U1jWz+3SlC8t3ukBYRHpOAUpE/MInh0txmrjQ/2krRCVBVGLfFCYifkkBSkT8wvrcEqLDgpiYGHXlQaYJp7dq9klEekwBSkR8nsNp8snhUualxmKzGVceeDYfagoUoESkxxSgRMTn7T5dSVV9i2vLdwDDFaBEpGcUoETE563LKSHAZnDdmA7aF4AVoALDIG583xQmIn7L9wNU/k74833QeNbTlYiIh6zPLWX6iEFEhQZ2PPD0Fhg2HQLsfVOYiPgt3w9QpgMOr4acLE9XIiIeUHi2gezC6s7bFzTVQvEB7X8SEbfw/QA1bAZEDYcDf/N0JSLiAetzSgEX2hec2QGmU/ufRMQtfD9AGQZMWAzH10NduaerEZE+ti6nhMSBoYyJC+94YN42wLD+0iUi0kO+H6AAJtwFzlbIXuXpSkSkDzW1Oth0tIwF4+IwjA7aF4C1/ykuHUI66BMlIuIi/whQQydCdIqW8UT6ma3HK2hocTB/XCen75xOyN8OSTP7pjAR8Xv+EaAMw5qFOrkRaoo8XY2I9JF1OSUE221cMzqm44Gl2dBUDcOv7pvCRMTvdTtAGYYxxDCM3e4spkfG3wmYcPBdT1ciIn3ANE3W55ZwbXI0oUEBHQ9uu0BYM1Ai4h49mYH6ORDqrkJ6LG4cDJkAB9/2dCUi0gdOlNVxqry+89N3YN1/FxYHg0b1fmEi0i90K0AZhrEAqAO8a71s/GLrb5pVpz1diYj0snU5JQBkpLoQoPK2WrNPnW00FxFxUZcDlGEYQcBzwPc6GPOYYRg7DMPYUVpa2pP6umbCndbXg+/03WeKiEeszy1hTFw4SYMHdDywtgQqT2j/k4i4VXdmoL4HvGSaZtWVBpim+TvTNKebpjk9NraT0zHuNHg0JEzVaTwRP1fb1Mq2ExWuLd+17X9SA00RcZ/uBKhM4EnDMDYAkw3DeNW9JfXQhLugcC+UHfV0JSLSSzYeKaPFYXZ+fQtY/Z8CgiF+Uu8XJiL9RpcDlGmac03TzDBNMwPYY5rm19xfVg+MX2x91WZyEb+1PqeEiBA700YM6nhgUy3sfRNGZ4A9uC9KE5F+okd9oM6FKO8SlQjDr4X9fwXT9HQ1IuJm59sXzB0TS2BAJ9/CdvwB6sth7nf6pjgR6Tf8o5HmpSbcCWW5UHLI05WIiJsdLKimpKap8+W75nrY/Ctr9kn9n0TEzfwzQKXfAYZNm8lF/ND6nBIMAzJSOzmgsmsF1JXA3Kf7pjAR6Vf8M0CFx8KoeVaA0jKeiF9Zl1vCxGEDiQnvYE9TSyNs+iWMmAMjZ/ddcSLSbz9yOCwAABSBSURBVPhngALrNF7lSSjY5elKRMRNymub2JNXxfzOZp92/wlqCmGeZp9EpHf4b4BKuxVsgXBAp/FE/MUnh0sxTTru/9TaDBuXWX2fRs3tu+JEpF/x3wAVOghSMq2u5E6np6sRETdYn1tKTHgwExKirjxo75+hOt+afdLVLSLSS3w+QO08VcGdL22isq758icn3AXVZ77oRCwiPqvV4eST3BLmp8Zis10hGDla4LNfWDcSJF/ftwWKSL/i8wFqQJCdvfln+cWa3MufTF0I9lCdxhPxA7tOV1Hd2Npx+4J9/2ddJj7vGc0+iUiv8vkAlRYfyUNXj+DPW09zsODsxU8Gh8PYm+DQu+Bo9UyBIuIW63JKsNsM5oyJaX+AoxU++zkMnWj9/15EpBf5fIACeCpzLAMHBPGjVQcxL21bMOFOqCuFk595pjgRcYsNuSXMGDmYyJDA9gcc+BtUHNfeJxHpE34RoKIGBPL0TalsP1nJyj0FFz855kYICtcynogPO1PVQE5RzZVP3zkd1uxT3HhIvaVvixORfskvAhTAvdOTmDQsip9kZVPbdMFyXWAojLsFsldZx5tFxOeszykBYP64K/R/OvQulB227ryz+c23NRHxYn7zncZmM/jRbeMpqWniVx8fufjJCXdB41k4ts4zxYlIj6zPKSFpcCjJseGXP+l0wqc/h5hUSL+974sTkX7JbwIUwJThg7hn2jD+sOkEx0prv3hi9HwIGahlPBEf1NjiYNOxMhakxmG0t7cp533r4vC53wVbQN8XKCL9kl8FKICnvzSOEHvAxRvK7UGQfhvkZlk3tIuIz9hyvJzGFmf77QtMEz79/9u78/CoykOP4993JiGQQDYIyL4JERJlaUBWBRRwpYC11EvVXqlW21pQr+1j29vqrbZcrtW2VtvaarXYKhbFFm0EkSVsioksEkggIktESDAsCdln3vvHGSQJYRlKcmYyv8/z5GE4ZzLzw+PJ+eW8Z94zD5L7Oh8YERFpJi2uQKW0i2HOxP6s3nmIpdsOnlyRfhNUl8HOpe6FE5GgrcgronW0hxF92p+6csfbcOCjwLVPOvskIs2nxRUogNtG9qR/p7b87M1tVNb4nIW9xkJcRw3jiYQRay3L84sY3bcDraO9DVfCqv+FxJ5w6c3uBBSRiNUiC1S018PDU9IoPFzBH1btchZ6vJA21TkDVVXqbkAROScfFx9nX0lF48N3Be/C/o0w9gHwnmZuKBGRJtIiCxTAqL4duP7SzjyzsoDCw4HrntJvgtpKyM90N5yInJOT0xc0KFAnzj4ldIdBt7iQTEQiXYstUAA/vH4AxsBjb213FnQbDvHdNIwnEiaW5xVxyUXt6JrYpv6KT1ZB4QYYM8f5kIiISDNr0QWqa2Ibvjv+YjK3HmDNzkPOBHvp05xT/+UlbscTkTM4VlnDB7tLGJfayPDdqnnQrgsMubX5g4mI0MILFMA3x/ahR3IsDy/Opcbnd4bx/DXO3DEiErLW7jxErd+eevuW3Wtgz1oYPRuiYtwJJyIRr8UXqNbRXn5yw0AKisp4cd1u6DwYkvtoGE8kxC3PKyKhTTRDeyTWX7FqnvOJ2i/d7k4wEREioEABXDWgI+NSU/jVsp0UlVVB2nT4JAvKityOJiKN8PstK/KLuaJ/ClHeOj+m9r7vXP80erZzn0sREZdERIEyxvDTG9OorvUzNzPPGcazftj2D7ejiUgjtu4/yqGyKsanNrh5cNY8iG0PGf/pTjARkYCIKFAAvTvEMWtsb17/8FNyKi+ClAEaxhMJUSvyijEGruxfp0AV5kDBMhh1L7SKcy+ciAgRVKAAvjv+Yi6Kb81P/5mLP2067F0PRwvdjiUiDSzPL2Jw90Tat61zkXjWPGiTBMO+6V4wEZGAiCpQcTFRPHTdJWz99Bhv+Uc6C3MXuRtKROo5VFbFlsIjTKg7fcFnm5373o34DsS0cy+ciEhARBUogCmDujC8dzI/WVNBbadBGsYTCTEr84uxtsHs46vmQUwCXH6Xe8FEROqIuAJljOGRKWkcrahhmXeMcy+tzz92O5aIBKzIL6JjuxjSusQ7Cw7mOvO2jbgbWie4G05EJCDiChTAgM7x3DqiJ49+kuos0DCeSEio8fnJ2lHM+NSOGGOchVn/B63aweV3uxtORKSOiCxQAPdPTKU8tgt50QOxGsYTCQk5ew5TWll7cviuOB9y33CG7mKT3Q0nIlJHxBaohNhoHpycyt/Kh2GKtkHRdrcjiUS8FXlFRHsNY/p1cBZkPQ7Rsc7F4yIiISRiCxTAVzO6s7vT1fjwUL15odtxRCLeivwihvdOpm1MlHNt4taFMGwWxLV3O5qISD0RXaC8HsN9U8ey3jeAspwFYK3bkUQiVuHhcnYcLGP8iekLVv8SvDHOxJkiIiEmogsUwJAeSezvfj3JlfvYt/09t+OIRKwVec69KSdc0hFKPoHNrzi3bGnb8SzfKSLS/CK+QAFMmDqLGutlS+bzWJ2FEml2ZVW1zH9vD73ax9K7QxyseRI8UTDqe25HExFplAoU0KHjRXyWMopBx5bzTu5nbscRiSg1Pj/f/uuHfFx8nEe+nI45ug82/Q2G3gbxnd2OJyLSKBWogC6j/4Nu5hCLFr9BZY3P7TgiEcFay48XbSVrRzGPTU13bh685lfOyjFz3A0nInIGKlABUQNuwO9pxfDjK3k2a5fbcUQiwtMrCliQvY97J1zM14b3gGP7YeN8GDITErq5HU9E5LSCLlDGmARjTKYxZqkxZpExplVTBGt2rePxpE5meswGfrdiB4WHy91OJNKiLdpYyONLdzB9SFfun9jfWbj212D9MOZ+d8OJiJzF+ZyBmgk8Ya2dBBwArrmwkVyUfhMJvsMMN9t47C1NrCnSVNYVHOL7C7cwsk975t50mXPbltKDkPMCDPoaJPV0O6KIyBkFXaCstc9Ya98J/DUFKLqwkVzUbzJEx/Fg11wytx5gbcEhtxOJtDg7DpbyrZdy6NU+jt/f+iVaRXngyF548z7wVevsk4iEhfO+BsoYMxJIstaeMnmSMeYuY0y2MSa7uLj43wrYrFrFwiXXkXZ0Jb2TWvHTf+bqgnKRC+jgsUq+8fwG2kR7eeGO4SSU7YJF98BvhsDOJTDuh9C+r9sxRUTO6rwKlDEmGXgKuKOx9dbaZ621GdbajJSUlH8nX/NLvwlTcZgnhx2moKiMqU+vJe/AMbdTiYS9sqpa7njhA45U1PC362PouuQuePpyyF0Ew+6E2Zvhygfdjikick7O5yLyVsDfgYestXsufCSX9Z0ArRMYfHQ5z38jg0NlVUx5ai1/zNqF369JNkXOR63Pz3deyiH+4Pus6fIUF79xA+xaBWMfgPu2wrVz9ak7EQkr53MGahYwFPiRMWalMWbGBc7krqgYGHAjbH+TCX0TWDLnCq5MTeGxf21n5p/eZ/+RCrcTioQV6/fz1/l/4Ht7vsPL0T8juTQfrn7YKU5X/TfEdXA7oohI0ExT37okIyPDZmdnN+l7XHAfL4f502DGSzDgRqy1vJq9j0cWb8PrMTw6NZ0vD+7qdkqR0OarhW1vcOjtuXQ4vpOjMZ1JuOoBGPJ1iG7jdjoRkUYZY3KstRlne54m0mxMrysgtgNsfQ0AYwwzhvUgc/ZY+nVsy+xXNnHvyxs5Wl7jclCREFRb5UxH8NsMeG0WR0qP81Lnh4h/cAsMv1PlSURahCi3A4QkbxSkTYWNf4WSXZDcB4Ce7eN49Vsj+d3Kj/n1uzvJ3l3C4zcPYvTFGoIQoaoMPnwR1j0FpZ9RlpzOD2rvo6T7RF6YNQIT5XU7oYjIBaMhvNP59EN4bhL4a6DbMEj/CqRNg3adANhSeIQ5Czaxq/g4s8b05sHJqbSO1gFCIlB5CWz4I7z/O6g4DL3GUph+D9e+6aVTfBteu3sUCbHRbqcUETkn5zqEpwJ1Jof3QO7r8NFrcPAjMB7oNRYu/QoMuJEKbzy/yNzOX9bvoX+ntvxqxhAGdol3O7VI8yg9AOt/C9l/huoy6H8NjLmfosRBTHtmHdU+P4u+PYpuSbFuJxUROWcqUBdaUZ5zTdTWhc6wnica+k2E9JvI8mTwwBsFHCmv5oFJqdw5tg9ej3E7sUjTOLIX1jzpDHH7ayBtOoy5Dy5K53hVLTOeXc+u4uMsuGskl3ZLcDutiEhQVKCairWwf2OgTL0OpfshOpaqvpN57shQntzdkyG9O/LEVwfpN29pWWqrYO1vYPXjzg1/B90Co2d/MXN4rc/PnX/JZtWOYp67fRjjL+nocmARkeCpQDUHvx/2rnPKVO4bUFFCdXQ8i6szyGQ01025mWlDezg3ShUJZ7tWwlsPwOcFMPDLMPnn9Sa+tNbyw0VbeXnDXh6bls7My3UzYBEJTypQzc1X4xxkPlqIf/tiPDXHKbKJ5CZNIOOGO2nXdySoSEm4KT0AS37kDF0n9YbrHod+V5/ytGdWFjDv7XzuGdeXH1xziQtBRUQuDBUoN9VU4Mtfwu6VL9KteDUxpoaKtt1pM/hm5wL0TmluJxQ5M78PPvgTLH8UaithzP0wZk6jczj9Y9OnzH5lEzcO6sKvZwzGo+v/RCSMqUCFiG2f7GPxgj8y4vgKxnpz8eCDlAEw9FbnGpLYZLcjitRXmANvzoEDW6DPeLj+l19c59TQe7s+57bnNjC4RyLzZw0nRnM9iUiYU4EKIZU1PuZm5vHPdVu4PXETd8Z/QGxRDkS1dj7BlHEHdMvQEJ+4q+IwLHvEmUW8bSe45hfO3Gen+f+yoKiU6c+sI6VdDK/dM4rE2FbNm1dEpAmoQIWg1TuL+a+/b6aotIrLovZxi2cZN5rVxFFJHr143TOZZd4rqPLG4vGA1xg8HoPXGLweg+fEnx6D11BvWb31xuD1NFhf57U8Hho8t8H31Xtuw++nToYG+b54TIOsDd/rTO9/MuOpuU+uN6b+evk3WAubX4GlP4aKErj8bhj3ELQ+/ZxmRaWVTHt6HVW1zlxP3ZP1iVMRaRlUoELUkfJq5q/fQ2lVLT6/xVtTRnrJUoYVL6JzZQGVnlhyEiaxNnEKha364LMWv9/i81v81vnTZ/li2Yn1flt/+cnnBr7fWvx+Tlnm8zdYH1gWbuoVr0aL34lC15TF9NQC2njZBM+J13C5mEZ/nk/csu8TtW89/q4Z1F77SzydL8PrMaf99Gh5dS0z/vAeBUVlLPjWCC7rltjMW1tEpOmoQIUba6EwG7Kfd2Y/r62EbsNh2CwYOBWiWzdrnHoF64uixSnL6q23Fl+gpJ1S4BqUtIYF0NfgNU6WwrrP5TTF78QyzpCrwfpzLKanvP4ZCmjd12/0v18I9dI2VDI7ahGzvP/iOK2ZW3sLC3zjsHXuL25MY2XT2b4VNT6evTWDqwd2cvFfISJy4alAhbPyEtj8slOmPi+ANkkweKZzrdRpLuaV0GetxdpTS2izFlOfn4sOvMuXts0lrvIAO7pMZUPfeymPTjptAT35uifXX9E/hYkqTyLSAqlAtQTWwu7V8MFzkPcm+Guh95XOWanU68CrG7RKEA7vhswfwI63oeNAuP4J6DnS7VQiIiHlXAtUVHOEkfNkDPS+wvkqPQgb/wI5L8Krtzmfkhp6Gwy9HRK7u51UQlltFax7CrIed26IPelR50JxFXARkfOmM1Dhxu+Dne84w3s7lzolq99kZ3jv4qvAo3l4pI5dqwK3YNkJA6Y4UxPUuQWLiIjUpzNQLZXHC6nXOF9H9jpz9nw4H3ZkQnxXiO/idkIJFb5q+GwzJPWCmQuh30S3E4mItBg6A9US1FZD/lvw0UKoKXc7jYSS7iNg9PcavQWLiIicSmegIklUK2fG6LRpbicRERGJCJ6zP0VERERE6lKBEhEREQmSCpSIiIhIkFSgRERERIKkAiUiIiISJBUoERERkSCpQImIiIgESQVKREREJEgqUCIiIiJBUoESERERCZIKlIiIiEiQVKBEREREgqQCJSIiIhIkY61t2jcwphjY06RvAh2AQ038HtI0tO3Ck7ZbeNJ2C1/ads2np7U25WxPavIC1RyMMdnW2gy3c0jwtO3Ck7ZbeNJ2C1/adqFHQ3giIiIiQVKBEhEREQlSSylQz7odQM6btl140nYLT9pu4UvbLsS0iGugRERERJpTSzkDJSIiItJsVKCk2Rljoowxe40xKwNfl7qdSaSlMsZ0MsasDjzuaowprLPvnfWj2iLSuLAvUMaY54wx640xP3Y7i5yzy4CXrbXjAl8fuR1Izq7BgTjaGLPYGLPWGHOH29mkccaYJOBFIC6w6HLgsTr7XrF76eR0jDEJxphMY8xSY8wiY0wrHetCT1gXKGPMdMBrrR0J9DHG9HM7k5yTEcANxpgNgR8KUW4HkjNr5EB8L5BjrR0NfMUY0861cHImPmAGcCzw9xHAN40xHxpjfu5eLDmLmcAT1tpJwAHga+hYF3LCukAB44BXA4+XAmPciyJB+AC42lo7HIgGrnM5j5xdwwPxOE7ue1mAJvgLQdbaY9bao3UWZeJsu2HASGPMZa4EkzOy1j5jrX0n8NcU4OvoWBdywr1AxQGfBh6XAJ1czCLnbou19rPA42xAv02FuEYOxNr3wtM6a22ptdYHbET7XkgzxowEkoB9aH8LOeFeoMqANoHHbQn/f0+kmG+MGWSM8QJTgc1uB5Kgad8LT0uMMZ2NMbHAJGCr24GkccaYZOAp4A60v4WkcN8IOZw8lTkI2O1eFAnC/wDzgU3AemvtMpfzSPC074WnR4AVwHvA7621+S7nkUYYY1oBfwcestbuQftbSArriTSNMfHAauBd4FpgRINhBhG5gIwxK62144wxPYF/AcuAUTj7ns/ddCItgzHmHuDnnDw7/2fgfnSsCylhXaDgi08HTQSyrLUH3M4jEimMMV1wfiteoh/mIk1Lx7rQE/YFSkRERKS5hfs1UCIiIiLNTgVKREREJEgqUCIiIiJBUoESERERCZIKlIiIiEiQ/h8hfblqXboQxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(dfa,label='非周末')\n",
    "plt.plot(dfb,label='周末')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、图书分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 爬取某图书网站，得到图书数据，6万条左右"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql\n",
    "conn=pymysql.connect(host='127.0.0.1',user='root',password='',db='doubandb',charset='utf8')\n",
    "sql='select * from books'\n",
    "data=pd.read_sql(sql,conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns=['ID', '书名', '作者', '出版社', 'org_name', 'translator', '出版时间',\n",
    "       '数', '价格', 'binding', 'series', 'ISBN', '评分',\n",
    "       '评论数量']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=data.drop(['ID','org_name','translator','binding','series'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>书名</th>\n",
       "      <th>作者</th>\n",
       "      <th>出版社</th>\n",
       "      <th>出版时间</th>\n",
       "      <th>数</th>\n",
       "      <th>价格</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>评分</th>\n",
       "      <th>评论数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td>1999-8</td>\n",
       "      <td>297</td>\n",
       "      <td>35.00元</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td>2002-10</td>\n",
       "      <td>203</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td>2001-10-1</td>\n",
       "      <td>223</td>\n",
       "      <td>20.00元</td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td>2000-01</td>\n",
       "      <td></td>\n",
       "      <td>20.00</td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td>2004-03</td>\n",
       "      <td>957</td>\n",
       "      <td>98.00</td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>尼采、海德格尔与德里达</td>\n",
       "      <td>[德] 恩斯特·贝勒尔</td>\n",
       "      <td>社会科学文献出版社</td>\n",
       "      <td>2001-10</td>\n",
       "      <td>212</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787801495655</td>\n",
       "      <td>7.8</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>民族国家与经济政策</td>\n",
       "      <td>韦伯(德)</td>\n",
       "      <td>生活·读书·新知三联书店</td>\n",
       "      <td>1997</td>\n",
       "      <td>141</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9787108010964</td>\n",
       "      <td>9.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>昆虫记</td>\n",
       "      <td>[法]\\n            J·H·法布尔</td>\n",
       "      <td>作家出版社</td>\n",
       "      <td>2004-03</td>\n",
       "      <td>352</td>\n",
       "      <td>19.00</td>\n",
       "      <td>9787506312820</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>古尊宿语录(上下册)</td>\n",
       "      <td>[宋]赜藏主编集</td>\n",
       "      <td>中华书局</td>\n",
       "      <td>1994-05</td>\n",
       "      <td>988</td>\n",
       "      <td>41.00</td>\n",
       "      <td>9787101009569</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>交往行为理论</td>\n",
       "      <td>[德]\\n            尤尔根·哈贝马斯</td>\n",
       "      <td>上海人民出版社</td>\n",
       "      <td>2004-8-1</td>\n",
       "      <td>446</td>\n",
       "      <td>39.00</td>\n",
       "      <td>9787208051485</td>\n",
       "      <td>8.2</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>末代皇帝的后半生</td>\n",
       "      <td>贾英华</td>\n",
       "      <td>人民文学出版社</td>\n",
       "      <td>2004-08</td>\n",
       "      <td>454</td>\n",
       "      <td>24.00</td>\n",
       "      <td>9787020045891</td>\n",
       "      <td>6.6</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>世界摄影大师・布列松</td>\n",
       "      <td>狄源沧</td>\n",
       "      <td>江苏美术出版社</td>\n",
       "      <td>1998年6月第1版</td>\n",
       "      <td>32</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787534405501</td>\n",
       "      <td>8.8</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             书名                         作者           出版社        出版时间    数  \\\n",
       "0         政治无意识                  弗雷德里克.詹姆逊     中国社会科学出版社      1999-8  297   \n",
       "1          生死遗言                        伊能静         现代出版社     2002-10  203   \n",
       "2        游泳技巧图解                   高桥雄介/吉村丰     北京体育大学出版社   2001-10-1  223   \n",
       "3        炒股必知必读                   刘超/周晓烨主编       当代世界出版社     2000-01        \n",
       "4          公关圣经                 (美)菲利普.莱斯礼       汕头大学出版社     2004-03  957   \n",
       "5   尼采、海德格尔与德里达                [德] 恩斯特·贝勒尔     社会科学文献出版社     2001-10  212   \n",
       "6     民族国家与经济政策                      韦伯(德)  生活·读书·新知三联书店        1997  141   \n",
       "7           昆虫记   [法]\\n            J·H·法布尔         作家出版社     2004-03  352   \n",
       "8    古尊宿语录(上下册)                   [宋]赜藏主编集          中华书局     1994-05  988   \n",
       "9        交往行为理论  [德]\\n            尤尔根·哈贝马斯       上海人民出版社    2004-8-1  446   \n",
       "10     末代皇帝的后半生                        贾英华       人民文学出版社     2004-08  454   \n",
       "11   世界摄影大师・布列松                        狄源沧       江苏美术出版社  1998年6月第1版   32   \n",
       "\n",
       "        价格           ISBN   评分  评论数量  \n",
       "0   35.00元  9787500425564  7.5   107  \n",
       "1   18.00元  9787800288494  7.4  2377  \n",
       "2   20.00元  9787810514156  8.4    42  \n",
       "3    20.00  9787801154194  0.0     0  \n",
       "4    98.00  9787810367004  7.7    44  \n",
       "5   18.00元  9787801495655  7.8    71  \n",
       "6     8.50  9787108010964  9.1   178  \n",
       "7    19.00  9787506312820  8.6  4448  \n",
       "8    41.00  9787101009569  9.0    61  \n",
       "9    39.00  9787208051485  8.2   125  \n",
       "10   24.00  9787020045891  6.6    63  \n",
       "11  18.00元  9787534405501  8.8   571  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 对数据做清洗(缺失值与异常值)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51363"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mydata[mydata['评分']!=0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=pd.to_datetime(mydata['出版时间'],errors='coerce')#出版时间转换成pandas时间时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata['出版时间']=s#更新出版时间为pandas时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>书名</th>\n",
       "      <th>作者</th>\n",
       "      <th>出版社</th>\n",
       "      <th>出版时间</th>\n",
       "      <th>数</th>\n",
       "      <th>价格</th>\n",
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       "      <th>评分</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td>1999-08-01</td>\n",
       "      <td>297</td>\n",
       "      <td>35.00元</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>203</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td>2001-10-01</td>\n",
       "      <td>223</td>\n",
       "      <td>20.00元</td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td></td>\n",
       "      <td>20.00</td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>957</td>\n",
       "      <td>98.00</td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>尼采、海德格尔与德里达</td>\n",
       "      <td>[德] 恩斯特·贝勒尔</td>\n",
       "      <td>社会科学文献出版社</td>\n",
       "      <td>2001-10-01</td>\n",
       "      <td>212</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787801495655</td>\n",
       "      <td>7.8</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>民族国家与经济政策</td>\n",
       "      <td>韦伯(德)</td>\n",
       "      <td>生活·读书·新知三联书店</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>141</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9787108010964</td>\n",
       "      <td>9.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>昆虫记</td>\n",
       "      <td>[法]\\n            J·H·法布尔</td>\n",
       "      <td>作家出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>352</td>\n",
       "      <td>19.00</td>\n",
       "      <td>9787506312820</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>古尊宿语录(上下册)</td>\n",
       "      <td>[宋]赜藏主编集</td>\n",
       "      <td>中华书局</td>\n",
       "      <td>1994-05-01</td>\n",
       "      <td>988</td>\n",
       "      <td>41.00</td>\n",
       "      <td>9787101009569</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>交往行为理论</td>\n",
       "      <td>[德]\\n            尤尔根·哈贝马斯</td>\n",
       "      <td>上海人民出版社</td>\n",
       "      <td>2004-08-01</td>\n",
       "      <td>446</td>\n",
       "      <td>39.00</td>\n",
       "      <td>9787208051485</td>\n",
       "      <td>8.2</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>末代皇帝的后半生</td>\n",
       "      <td>贾英华</td>\n",
       "      <td>人民文学出版社</td>\n",
       "      <td>2004-08-01</td>\n",
       "      <td>454</td>\n",
       "      <td>24.00</td>\n",
       "      <td>9787020045891</td>\n",
       "      <td>6.6</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>世界摄影大师・布列松</td>\n",
       "      <td>狄源沧</td>\n",
       "      <td>江苏美术出版社</td>\n",
       "      <td>NaT</td>\n",
       "      <td>32</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787534405501</td>\n",
       "      <td>8.8</td>\n",
       "      <td>571</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             书名                         作者           出版社       出版时间    数  \\\n",
       "0         政治无意识                  弗雷德里克.詹姆逊     中国社会科学出版社 1999-08-01  297   \n",
       "1          生死遗言                        伊能静         现代出版社 2002-10-01  203   \n",
       "2        游泳技巧图解                   高桥雄介/吉村丰     北京体育大学出版社 2001-10-01  223   \n",
       "3        炒股必知必读                   刘超/周晓烨主编       当代世界出版社 2000-01-01        \n",
       "4          公关圣经                 (美)菲利普.莱斯礼       汕头大学出版社 2004-03-01  957   \n",
       "5   尼采、海德格尔与德里达                [德] 恩斯特·贝勒尔     社会科学文献出版社 2001-10-01  212   \n",
       "6     民族国家与经济政策                      韦伯(德)  生活·读书·新知三联书店 1997-01-01  141   \n",
       "7           昆虫记   [法]\\n            J·H·法布尔         作家出版社 2004-03-01  352   \n",
       "8    古尊宿语录(上下册)                   [宋]赜藏主编集          中华书局 1994-05-01  988   \n",
       "9        交往行为理论  [德]\\n            尤尔根·哈贝马斯       上海人民出版社 2004-08-01  446   \n",
       "10     末代皇帝的后半生                        贾英华       人民文学出版社 2004-08-01  454   \n",
       "11   世界摄影大师・布列松                        狄源沧       江苏美术出版社        NaT   32   \n",
       "\n",
       "        价格           ISBN   评分  评论数量  \n",
       "0   35.00元  9787500425564  7.5   107  \n",
       "1   18.00元  9787800288494  7.4  2377  \n",
       "2   20.00元  9787810514156  8.4    42  \n",
       "3    20.00  9787801154194  0.0     0  \n",
       "4    98.00  9787810367004  7.7    44  \n",
       "5   18.00元  9787801495655  7.8    71  \n",
       "6     8.50  9787108010964  9.1   178  \n",
       "7    19.00  9787506312820  8.6  4448  \n",
       "8    41.00  9787101009569  9.0    61  \n",
       "9    39.00  9787208051485  8.2   125  \n",
       "10   24.00  9787020045891  6.6    63  \n",
       "11  18.00元  9787534405501  8.8   571  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=mydata.dropna(subset=['出版时间'])#去掉无时间的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>评分</th>\n",
       "      <th>评论数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td>1999-08-01</td>\n",
       "      <td>297</td>\n",
       "      <td>35.00元</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>203</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td>2001-10-01</td>\n",
       "      <td>223</td>\n",
       "      <td>20.00元</td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td></td>\n",
       "      <td>20.00</td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>957</td>\n",
       "      <td>98.00</td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>尼采、海德格尔与德里达</td>\n",
       "      <td>[德] 恩斯特·贝勒尔</td>\n",
       "      <td>社会科学文献出版社</td>\n",
       "      <td>2001-10-01</td>\n",
       "      <td>212</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>9787801495655</td>\n",
       "      <td>7.8</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>民族国家与经济政策</td>\n",
       "      <td>韦伯(德)</td>\n",
       "      <td>生活·读书·新知三联书店</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>141</td>\n",
       "      <td>8.50</td>\n",
       "      <td>9787108010964</td>\n",
       "      <td>9.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>昆虫记</td>\n",
       "      <td>[法]\\n            J·H·法布尔</td>\n",
       "      <td>作家出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>352</td>\n",
       "      <td>19.00</td>\n",
       "      <td>9787506312820</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>古尊宿语录(上下册)</td>\n",
       "      <td>[宋]赜藏主编集</td>\n",
       "      <td>中华书局</td>\n",
       "      <td>1994-05-01</td>\n",
       "      <td>988</td>\n",
       "      <td>41.00</td>\n",
       "      <td>9787101009569</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>交往行为理论</td>\n",
       "      <td>[德]\\n            尤尔根·哈贝马斯</td>\n",
       "      <td>上海人民出版社</td>\n",
       "      <td>2004-08-01</td>\n",
       "      <td>446</td>\n",
       "      <td>39.00</td>\n",
       "      <td>9787208051485</td>\n",
       "      <td>8.2</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>末代皇帝的后半生</td>\n",
       "      <td>贾英华</td>\n",
       "      <td>人民文学出版社</td>\n",
       "      <td>2004-08-01</td>\n",
       "      <td>454</td>\n",
       "      <td>24.00</td>\n",
       "      <td>9787020045891</td>\n",
       "      <td>6.6</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>迎向灵光消逝的年代</td>\n",
       "      <td>[德]\\n            瓦尔特·本雅明</td>\n",
       "      <td>广西师范大学出版社</td>\n",
       "      <td>2004-08-01</td>\n",
       "      <td>158</td>\n",
       "      <td>16.00</td>\n",
       "      <td>9787563345137</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>王尔德全集</td>\n",
       "      <td>[爱尔兰]\\n            奥斯卡·王尔德</td>\n",
       "      <td>中国文学出版社</td>\n",
       "      <td>2000-09-01</td>\n",
       "      <td>3472</td>\n",
       "      <td>198.00</td>\n",
       "      <td>9787507104455</td>\n",
       "      <td>9.0</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>袁氏当国</td>\n",
       "      <td>[美]\\n            唐德刚</td>\n",
       "      <td>广西师范大学出版社</td>\n",
       "      <td>2004-11-01</td>\n",
       "      <td>209</td>\n",
       "      <td>17.00</td>\n",
       "      <td>9787563350032</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>音乐剧之旅</td>\n",
       "      <td>周小川/肖梦</td>\n",
       "      <td>新世界出版社</td>\n",
       "      <td>2002-04-01</td>\n",
       "      <td>581</td>\n",
       "      <td>36.00</td>\n",
       "      <td>9787800054433</td>\n",
       "      <td>7.2</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>色彩学讲座</td>\n",
       "      <td>李萧锟</td>\n",
       "      <td>广西师范大学出版社</td>\n",
       "      <td>2003-03-01</td>\n",
       "      <td>124</td>\n",
       "      <td>39.80</td>\n",
       "      <td>9787563338764</td>\n",
       "      <td>7.8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>一个狗娘养的自白</td>\n",
       "      <td>(美)艾伦.纽哈斯</td>\n",
       "      <td>东方出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>291</td>\n",
       "      <td>25.00</td>\n",
       "      <td>9787506018586</td>\n",
       "      <td>7.8</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>电视人</td>\n",
       "      <td>[日]\\n            村上春树</td>\n",
       "      <td>上海译文出版社</td>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>134</td>\n",
       "      <td>12.00元</td>\n",
       "      <td>9787532729951</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>人·岁月·生活（全三册）</td>\n",
       "      <td>[苏联]\\n            伊利亚·爱伦堡</td>\n",
       "      <td>海南出版社</td>\n",
       "      <td>1999-10-01</td>\n",
       "      <td></td>\n",
       "      <td>88.00元</td>\n",
       "      <td>9787806455760</td>\n",
       "      <td>8.7</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>水母与蜗牛</td>\n",
       "      <td>[美]\\n            刘易斯·托马斯</td>\n",
       "      <td>湖南科学技术出版社</td>\n",
       "      <td>1996-10-01</td>\n",
       "      <td>136</td>\n",
       "      <td>9.00元</td>\n",
       "      <td>9787535718556</td>\n",
       "      <td>8.5</td>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              书名                          作者           出版社       出版时间     数  \\\n",
       "0          政治无意识                   弗雷德里克.詹姆逊     中国社会科学出版社 1999-08-01   297   \n",
       "1           生死遗言                         伊能静         现代出版社 2002-10-01   203   \n",
       "2         游泳技巧图解                    高桥雄介/吉村丰     北京体育大学出版社 2001-10-01   223   \n",
       "3         炒股必知必读                    刘超/周晓烨主编       当代世界出版社 2000-01-01         \n",
       "4           公关圣经                  (美)菲利普.莱斯礼       汕头大学出版社 2004-03-01   957   \n",
       "5    尼采、海德格尔与德里达                 [德] 恩斯特·贝勒尔     社会科学文献出版社 2001-10-01   212   \n",
       "6      民族国家与经济政策                       韦伯(德)  生活·读书·新知三联书店 1997-01-01   141   \n",
       "7            昆虫记    [法]\\n            J·H·法布尔         作家出版社 2004-03-01   352   \n",
       "8     古尊宿语录(上下册)                    [宋]赜藏主编集          中华书局 1994-05-01   988   \n",
       "9         交往行为理论   [德]\\n            尤尔根·哈贝马斯       上海人民出版社 2004-08-01   446   \n",
       "10      末代皇帝的后半生                         贾英华       人民文学出版社 2004-08-01   454   \n",
       "12     迎向灵光消逝的年代    [德]\\n            瓦尔特·本雅明     广西师范大学出版社 2004-08-01   158   \n",
       "13         王尔德全集  [爱尔兰]\\n            奥斯卡·王尔德       中国文学出版社 2000-09-01  3472   \n",
       "14          袁氏当国        [美]\\n            唐德刚     广西师范大学出版社 2004-11-01   209   \n",
       "15         音乐剧之旅                      周小川/肖梦        新世界出版社 2002-04-01   581   \n",
       "16         色彩学讲座                         李萧锟     广西师范大学出版社 2003-03-01   124   \n",
       "17      一个狗娘养的自白                   (美)艾伦.纽哈斯         东方出版社 2004-03-01   291   \n",
       "18           电视人       [日]\\n            村上春树       上海译文出版社 2002-12-01   134   \n",
       "19  人·岁月·生活（全三册）   [苏联]\\n            伊利亚·爱伦堡         海南出版社 1999-10-01         \n",
       "20         水母与蜗牛    [美]\\n            刘易斯·托马斯     湖南科学技术出版社 1996-10-01   136   \n",
       "\n",
       "        价格           ISBN   评分  评论数量  \n",
       "0   35.00元  9787500425564  7.5   107  \n",
       "1   18.00元  9787800288494  7.4  2377  \n",
       "2   20.00元  9787810514156  8.4    42  \n",
       "3    20.00  9787801154194  0.0     0  \n",
       "4    98.00  9787810367004  7.7    44  \n",
       "5   18.00元  9787801495655  7.8    71  \n",
       "6     8.50  9787108010964  9.1   178  \n",
       "7    19.00  9787506312820  8.6  4448  \n",
       "8    41.00  9787101009569  9.0    61  \n",
       "9    39.00  9787208051485  8.2   125  \n",
       "10   24.00  9787020045891  6.6    63  \n",
       "12   16.00  9787563345137  8.5  1771  \n",
       "13  198.00  9787507104455  9.0   783  \n",
       "14   17.00  9787563350032  8.5  6267  \n",
       "15   36.00  9787800054433  7.2    26  \n",
       "16   39.80  9787563338764  7.8    29  \n",
       "17   25.00  9787506018586  7.8   244  \n",
       "18  12.00元  9787532729951  7.7  3524  \n",
       "19  88.00元  9787806455760  8.7   274  \n",
       "20   9.00元  9787535718556  8.5   676  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=mydata[mydata['出版社']!='']#去掉无出版社信息的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=mydata[mydata['作者']!='']#去掉无作者信息的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mydata[mydata['作者']==''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=pd.to_numeric(mydata['价格'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata['价格']=n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=mydata.dropna(subset=['价格'])#去掉无价格的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11439"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mydata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 分析书的数量与年份的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata.index=mydata['出版时间']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>书名</th>\n",
       "      <th>作者</th>\n",
       "      <th>出版社</th>\n",
       "      <th>出版时间</th>\n",
       "      <th>数</th>\n",
       "      <th>价格</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>评分</th>\n",
       "      <th>评论数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>出版时间</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td></td>\n",
       "      <td>20.0</td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-01</th>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>957</td>\n",
       "      <td>98.0</td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>民族国家与经济政策</td>\n",
       "      <td>韦伯(德)</td>\n",
       "      <td>生活·读书·新知三联书店</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>141</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9787108010964</td>\n",
       "      <td>9.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-01</th>\n",
       "      <td>昆虫记</td>\n",
       "      <td>[法]\\n            J·H·法布尔</td>\n",
       "      <td>作家出版社</td>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>352</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9787506312820</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-05-01</th>\n",
       "      <td>古尊宿语录(上下册)</td>\n",
       "      <td>[宋]赜藏主编集</td>\n",
       "      <td>中华书局</td>\n",
       "      <td>1994-05-01</td>\n",
       "      <td>988</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9787101009569</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    书名                        作者           出版社       出版时间  \\\n",
       "出版时间                                                                        \n",
       "2000-01-01      炒股必知必读                  刘超/周晓烨主编       当代世界出版社 2000-01-01   \n",
       "2004-03-01        公关圣经                (美)菲利普.莱斯礼       汕头大学出版社 2004-03-01   \n",
       "1997-01-01   民族国家与经济政策                     韦伯(德)  生活·读书·新知三联书店 1997-01-01   \n",
       "2004-03-01         昆虫记  [法]\\n            J·H·法布尔         作家出版社 2004-03-01   \n",
       "1994-05-01  古尊宿语录(上下册)                  [宋]赜藏主编集          中华书局 1994-05-01   \n",
       "\n",
       "              数    价格           ISBN   评分  评论数量  \n",
       "出版时间                                             \n",
       "2000-01-01       20.0  9787801154194  0.0     0  \n",
       "2004-03-01  957  98.0  9787810367004  7.7    44  \n",
       "1997-01-01  141   8.5  9787108010964  9.1   178  \n",
       "2004-03-01  352  19.0  9787506312820  8.6  4448  \n",
       "1994-05-01  988  41.0  9787101009569  9.0    61  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=mydata.groupby(mydata.index.year).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 书的数量与年份的关系\n",
    "plt.figure(figsize=(10,6))\n",
    "df3['书名'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分析书籍的评分与年代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=mydata['评分'].groupby(mydata.index.year).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 书的评分与年份的关系\n",
    "plt.figure(figsize=(10,6))\n",
    "df4.plot()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 书籍的价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先把价格异常值去掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_upper=mydata['价格'].quantile(0.75)\n",
    "q_lower=mydata['价格'].quantile(0.25)\n",
    "k=1.5\n",
    "val=q_upper-q_lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "933"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mydata[(mydata['价格']>q_upper+k*val) | (mydata['价格']<q_lower-k*val)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata=mydata[(mydata['价格']<q_upper+k*val) & (mydata['价格']>q_lower-k*val)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10506"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata[['价格']].hist(rwidth=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 出版的书籍最多的前20个出版社"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=mydata.groupby('出版社').count()#以出版社名字重组计数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=df4.sort_values(by='书名',ascending=False)#按数量高到低排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4['书名'][:20].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 书籍评分比较高出版社"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5=mydata.groupby('出版社')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6=df5['评分'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df6[:20].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 出书多的作者"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7=mydata.groupby('作者').count().sort_values(by='书名',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7['书名'][:20].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 评分高与评论数量这间是否存在某种关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8=mydata[['评分','评论数量']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8['评分']=pd.to_numeric(df8['评分'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8['评论数量']=pd.to_numeric(df8['评论数量'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8['评论数量'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df8.plot.scatter(x='评分',y='评论数量')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论: 评论数量多的评价高的几率大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
